Nonstationary function optimization using genetic algorithm with dominance and diploidy
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Genetic Algorithms in Noisy Environments
Machine Learning
Genetic Algorithms for Tracking Changing Environments
Proceedings of the 5th International Conference on Genetic Algorithms
Adaptation to a Changing Environment by Means of the Feedback Thermodynamical Genetic Algorithm
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Averaging Efficiently in the Presence of Noise
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Creating Robust Solutions by Means of Evolutionary Algorithms
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic algorithms with a robust solution searching scheme
IEEE Transactions on Evolutionary Computation
History and Immortality in Evolutionary Computation
Selected Papers from the 5th European Conference on Artificial Evolution
Hoeffding bound based evolutionary algorithm for symbolic regression
Engineering Applications of Artificial Intelligence
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In the present paper, optimization of functions with uncertainty by means of Genetic Algorithms (GA) is discussed. For such problems, there have been proposed methods of sampling fitness function several times and taking average of them for evaluation of each individual. However, important applications having uncertain fitness functions are online adaptation of real systems and complicated computer simulation using random variables. In such applications, possible number of fitness evaluation is quite limited. Hence, methods achieving optimization with less number of fitness evaluation is needed. In the present paper, the authors propose a GA for optimization of continuous fitness functions with observation noise utilizing history of search so as to reduce number of fitness evaluation. In the proposed method, value of fitness function at a novel search point is estimated not only by the sampled fitness value at that point but also by utilizing the fitness values of individuals stored in the history of search. Computer experiments using quadric fitness functions show that the proposed method outperforms the conventional GA of sampling fitness values several times at each search point in noisy environment.